bert-finetuned-ner

Proccyon/bert-finetuned-ner

bert-finetuned-ner is a token classification model from Proccyon. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: apache-2.0
Model type: bert
Access: Public
Created: Nov 5, 2023
Updated: Jan 1, 2024
SHA: e0a9f3d2

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Base models

Model card

From the Hugging Face model README

bert-finetuned-ner

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0209
Precision: 0.8249
Recall: 0.8825
F1: 0.8527
Accuracy: 0.9946
B-location-precision: 0.9446
B-location-recall: 0.9653
B-location-f1: 0.9549
I-location-precision: 0.9358
I-location-recall: 0.9745
I-location-f1: 0.9548
B-group-precision: 0.8819
B-group-recall: 0.8485
B-group-f1: 0.8649
I-group-precision: 0.8879
I-group-recall: 0.8358
I-group-f1: 0.8610
B-corporation-precision: 0.8475
B-corporation-recall: 0.8552
B-corporation-f1: 0.8514
I-corporation-precision: 0.8158
I-corporation-recall: 0.7294
I-corporation-f1: 0.7702
B-person-precision: 0.9583
B-person-recall: 0.9742
B-person-f1: 0.9662
I-person-precision: 0.9596
I-person-recall: 0.95
I-person-f1: 0.9548
B-creative-work-precision: 0.8102
B-creative-work-recall: 0.7929
B-creative-work-f1: 0.8014
I-creative-work-precision: 0.8131
I-creative-work-recall: 0.8354
I-creative-work-f1: 0.8241
B-product-precision: 0.8682
B-product-recall: 0.7887
B-product-f1: 0.8266
I-product-precision: 0.8862
I-product-recall: 0.8886
I-product-f1: 0.8874
Corporation-precision: 0.6972
Corporation-recall: 0.7919
Corporation-f1: 0.7415
Corporation-number: 221
Creative-work-precision: 0.6433
Creative-work-recall: 0.7214
Creative-work-f1: 0.6801
Creative-work-number: 140
Group-precision: 0.7465
Group-recall: 0.8144
Group-f1: 0.7790
Group-number: 264
Location-precision: 0.9026
Location-recall: 0.9471
Location-f1: 0.9243
Location-number: 548
Person-precision: 0.9101
Person-recall: 0.9515
Person-f1: 0.9304
Person-number: 660
Product-precision: 0.6908
Product-recall: 0.7394
Product-f1: 0.7143
Product-number: 142

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	B-location-precision	B-location-recall	B-location-f1	I-location-precision	I-location-recall	I-location-f1	B-group-precision	B-group-recall	B-group-f1	I-group-precision	I-group-recall	I-group-f1	B-corporation-precision	B-corporation-recall	B-corporation-f1	I-corporation-precision	I-corporation-recall	I-corporation-f1	B-person-precision	B-person-recall	B-person-f1	I-person-precision	I-person-recall	I-person-f1	B-creative-work-precision	B-creative-work-recall	B-creative-work-f1	I-creative-work-precision	I-creative-work-recall	I-creative-work-f1	B-product-precision	B-product-recall	B-product-f1	I-product-precision	I-product-recall	I-product-f1	Corporation-precision	Corporation-recall	Corporation-f1	Corporation-number	Creative-work-precision	Creative-work-recall	Creative-work-f1	Creative-work-number	Group-precision	Group-recall	Group-f1	Group-number	Location-precision	Location-recall	Location-f1	Location-number	Person-precision	Person-recall	Person-f1	Person-number	Product-precision	Product-recall	Product-f1	Product-number
No log	1.0	107	0.1175	0.5693	0.4076	0.4751	0.9701	0.6320	0.7646	0.6920	0.7752	0.3929	0.5215	1.0	0.0114	0.0225	0.6667	0.0176	0.0343	0.9787	0.2081	0.3433	nan	0.0	nan	0.8123	0.7409	0.7750	0.9117	0.555	0.6900	nan	0.0	nan	nan	0.0	nan	nan	0.0	nan	nan	0.0	nan	0.9787	0.2081	0.3433	221	0.0	0.0	0.0	140	0.3333	0.0152	0.0290	264	0.4682	0.6040	0.5275	548	0.6543	0.6424	0.6483	660	0.0	0.0	0.0	142
No log	2.0	214	0.0411	0.6931	0.7489	0.7199	0.9886	0.8194	0.9270	0.8699	0.8701	0.9214	0.8950	0.7919	0.5909	0.6768	0.6897	0.7625	0.7242	0.8297	0.6833	0.7494	0.8548	0.3118	0.4569	0.9139	0.9485	0.9309	0.8996	0.9075	0.9035	0.7541	0.3286	0.4577	0.7952	0.5091	0.6208	0.7407	0.5634	0.64	0.6740	0.8315	0.7445	0.6515	0.5837	0.6158	221	0.2941	0.2143	0.2479	140	0.5051	0.5682	0.5348	264	0.7617	0.8923	0.8218	548	0.8470	0.9227	0.8832	660	0.4091	0.5070	0.4528	142
No log	3.0	321	0.0209	0.8249	0.8825	0.8527	0.9946	0.9446	0.9653	0.9549	0.9358	0.9745	0.9548	0.8819	0.8485	0.8649	0.8879	0.8358	0.8610	0.8475	0.8552	0.8514	0.8158	0.7294	0.7702	0.9583	0.9742	0.9662	0.9596	0.95	0.9548	0.8102	0.7929	0.8014	0.8131	0.8354	0.8241	0.8682	0.7887	0.8266	0.8862	0.8886	0.8874	0.6972	0.7919	0.7415	221	0.6433	0.7214	0.6801	140	0.7465	0.8144	0.7790	264	0.9026	0.9471	0.9243	548	0.9101	0.9515	0.9304	660	0.6908	0.7394	0.7143	142

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cpu
Datasets 2.14.6
Tokenizers 0.14.1

Task: Token Classification
Library: transformers
Type: bert
License: apache-2.0
Created: Nov 5, 2023
Updated: Jan 1, 2024